Temperature detection using microwave radiometry has proven value for noninvasively measuring the absolute temperature of tissues inside the body. However, current clinical radiometers operate in the gigahertz range, which limits their depth of penetration. We have designed and built a noninvasive radiometer which operates at radio frequencies (64 MHz) with ∼100-kHz bandwidth, using an external RF loop coil as a thermal detector. The core of the radiometer is an accurate impedance measurement and automatic matching circuit of 0.05 Ω accuracy to compensate for any load variations. The radiometer permits temperature measurements with accuracy of ±0.1°K, over a tested physiological range of 28° C-40° C in saline phantoms whose electric properties match those of tissue. Because 1.5 T magnetic resonance imaging (MRI) scanners also operate at 64 MHz, we demonstrate the feasibility of integrating our radiometer with an MRI scanner to monitor RF power deposition and temperature dosimetry, obtaining coarse, spatially resolved, absolute thermal maps in the physiological range. We conclude that RF radiometry offers promise as a direct, noninvasive method of monitoring tissue heating during MRI studies and thereby providing an independent means of verifying patient-safe operation. Other potential applications include titration of hyper- and hypo-therapies.
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http://dx.doi.org/10.1109/TCSI.2006.884423 | DOI Listing |
Sci Rep
December 2024
School of Electrical and Information, Hunan University, Changsha, 410083, China.
Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting.
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December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
View Article and Find Full Text PDFConserv Physiol
December 2024
Department of Marine Bioscience, Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8564, Japan.
The physiological performance of ectotherms is influenced by temperature, raising concerns about the impact of global warming on ectotherms. Understanding the relationship between ecologically relevant temperatures and the physiological performance of ectotherms provides a basis for assessing their resilience to changing environments. Absolute aerobic scope (AAS) is a functional metric of the thermal performance of aquatic ectotherms.
View Article and Find Full Text PDFInt J Hyg Environ Health
December 2024
Department of Chemistry, Institute of Exact and Biological Sciences, Federal University of Ouro Preto (UFOP), Ouro Preto, 35450-000, Minas Gerais, Brazil.
Trimethoprim (TMP) and sulfamethoxazole (SMX) are bacteriostatic agents, which are co-administered to patients during infection treatment due to their synergetic effects. Once consumed, TMP and SMX end up in wastewater and are directed to municipal wastewater treatment plants (WWTPs) which fail to remove these contaminants from municipal wastewater. The discharge of WWTP effluents containing antibiotics in the environment is a major concern for public health as it contributes to the spread of antimicrobial resistance.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamil Nadu, India.
Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.
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